Experimental large-scale jet flames’ geometrical features extraction for risk management using infrared images and deep learning segmentation methods

Pérez-Guerrero, Carmina; Palacios, Adriana; Ochoa-Ruiz, Gilberto; Foroughi, Vahid; Pastor, Elsa; González-Mendoza, Miguel; Falcón-Morales, Luis E.

doi:10.1016/j.jlp.2022.104903

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Experimental large-scale jet flames’ geometrical features extraction for risk management using infrared images and deep learning segmentation methods

Cited by 3 publications

References 33 publications

Computer vision-based characterization of large-scale jet flames using a synthetic infrared image generation approach

Computer vision-based characterization of large-scale jet flames using a synthetic infrared image generation approach

Neodymium oxide concentration state recognition model in neodymium molten salt electrolysis process based on flame image features

Gaussian Process for the Machine Learning-based Smart Fire Detection System

Contact Info

Product

Resources

About